0% Complete
Home
/
14th International Conference on Computer and Knowledge Engineering
Deep Learning-Based Malaysian Sign Language (MSL) Recognition: Exploring the Impact of Color Spaces
Authors :
Ervin Gubin Moung
1
Precilla Fiona Suwek
2
Maisarah Mohd Sufian
3
Valentino Liaw
4
Ali Farzamnia
5
Wei Leong Khong
6
1- Faculty of Computing and Informatics University Malaysia Sabah
2- Faculty of Computing and Informatics University Malaysia Sabah
3- Faculty of Computing and Informatics Universiti Malaysia Sabah
4- Faculty of Computing and Informatics Universiti Malaysia Sabah
5- School of Computing and Engineering University of Huddersfield
6- School of Engineering Monash University Malaysia
Keywords :
sign language،Malaysian Sign Language،color space،ResNet18،Convolutional Neural Network (CNN)
Abstract :
Sign Language is one form of communication for this group of people to communicate with each other. Not only for people with hearing problems but sign language is also useful for people who are mute or have problem speaking. The most used sign language is the American Sign Language (ASL) that is widely used in English speaking countries. In Malaysia, Bahasa Isyarat Malaysia (BIM) or Malaysian Sign Language (MSL) is still new to the community in Malaysia. In this project, a dataset with 5980 images of the signed alphabet is used to train models to recognize what the signs mean. The problem this project aims to address is the limited research and available datasets in the field of Malaysian Sign Language (MSL) recognition using deep learning and various color spaces. Two models that are used are Convolutional Neural Network (CNN) and Residual Network 18 (ResNet18). The images are also converted into different color spaces which are RGB, YCbCr, Grayscale and the combination of RGB and YCbCr. From the results, RGB is the best color space with CNN without any image processing technique - 80% testing accuracy, with Histogram Equalization (HE) - 82.40% testing accuracy, and with Contrast Limited Adaptive HE (CLAHE) - 83.90%. Whereas YCbCr is the best color space when using ResNet18 without any image processing technique - 88% testing accuracy, with HE - 84.40% testing accuracy, and with CLAHE - 88.30%. The precision, recall, and F1-score metrics are also have been used to evaluate the efficacy of the suggested system.
Papers List
List of archived papers
Farsi Optical Character Recognition Using a Transformer-based Model
Fatemeh Asadi Zeydabadi - Elham Shabaninia - Hossein Nezamabadi-pour - Melika Shojaee
FarSick: A Persian Semantic Textual Similarity And Natural Language Inference Dataset
Zahra Ghasemi - Mohammad Ali Keyvanrad
Energy-Aware Dynamic Digital Twin Placement in Mobile Edge Computing
Mahdi Hematyar - Zeinab Movahedi
Optimizing MR Image Registration for Accurate Brain Volume Measurement in Children with Autism Spectrum Disorder
Shiva Sanati - Mahdi Saadatmand
Practical Implementation of Real-Time Waste Detection and Recycling based on Deep Learning for Delta Parallel Robot
Hasan Jalali - Shaya Garjani - Ahmad Kalhor - Mehdi Tale Masouleh - Parisa Yousefi
Leveraging Self-Supervised Models for Automatic Whispered Speech Recognition
Aref Farhadipour - Homa Asadi - Volker Dellwo
Machine and Deep Learning Models for Prediction of Small Molecule–Biotech Drug Pair’s Interactions
Fatemeh Nasiri - Mohsen Hooshmand
Designing a High Perfomance and High Profit P2P Energy Trading System Using a Consortium Blockchain Network
Poonia Taheri Makhsoos - Behnam Bahrak - Fattaneh Taghiyareh
Sensitivity Reliability Analysis of Power Distribution Networks Using Fuzzy Logic
Mohammed Wadi - Wisam Elmasry - Ismail Kucuk - Hossein Shahinzadeh
Energy Efficient Power Allocation in MIMO-NOMA Systems with ZF Receiver Beamforming in Multiple Clusters
Mahdi Nangir - Abdolrasoul Sakhaei Gharagezlou - Nima Imani
more
Samin Hamayesh - Version 41.5.3